Overview

Dataset statistics

Number of variables13
Number of observations562100
Missing cells40898
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.8 MiB
Average record size in memory104.0 B

Variable types

Numeric9
DateTime1
Categorical3

Warnings

Exit_Reason has constant value "AgentAnswered" Constant
Party_Name has a high cardinality: 147 distinct values High cardinality
QueuedTime is highly correlated with WaitTime and 1 other fieldsHigh correlation
RingTime is highly correlated with WaitTime vs QueuedTimeHigh correlation
WaitTime is highly correlated with QueuedTime and 1 other fieldsHigh correlation
Queue + Ring is highly correlated with QueuedTime and 1 other fieldsHigh correlation
WaitTime vs QueuedTime is highly correlated with RingTimeHigh correlation
channel is highly correlated with Exit_ReasonHigh correlation
Exit_Reason is highly correlated with channelHigh correlation
HoldTime has 24827 (4.4%) missing values Missing
WrapTime has 14518 (2.6%) missing values Missing
WrapTime is highly skewed (γ1 = 379.2244211) Skewed
df_index has unique values Unique
QueuedTime has 32969 (5.9%) zeros Zeros
TalkTime has 7681 (1.4%) zeros Zeros
HoldTime has 431308 (76.7%) zeros Zeros
WrapTime has 10922 (1.9%) zeros Zeros

Reproduction

Analysis started2021-02-26 15:34:18.647385
Analysis finished2021-02-26 15:34:58.891004
Duration40.24 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct562100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean313042.5419
Minimum13
Maximum613887
Zeros0
Zeros (%)0.0%
Memory size4.3 MiB
2021-02-26T16:34:59.158205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile31238.95
Q1164184.5
median316289.5
Q3465619.25
95-th percentile584446.05
Maximum613887
Range613874
Interquartile range (IQR)301434.75

Descriptive statistics

Standard deviation176709.6527
Coefficient of variation (CV)0.5644908567
Kurtosis-1.175729032
Mean313042.5419
Median Absolute Deviation (MAD)150688
Skewness-0.05459428694
Sum1.759612128 × 1011
Variance3.122630135 × 1010
MonotocityStrictly increasing
2021-02-26T16:34:59.285870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40981
 
< 0.1%
991731
 
< 0.1%
233901
 
< 0.1%
213431
 
< 0.1%
1073611
 
< 0.1%
1135061
 
< 0.1%
1114591
 
< 0.1%
1012201
 
< 0.1%
1053181
 
< 0.1%
602321
 
< 0.1%
Other values (562090)562090
> 99.9%
ValueCountFrequency (%)
131
< 0.1%
141
< 0.1%
161
< 0.1%
181
< 0.1%
191
< 0.1%
ValueCountFrequency (%)
6138871
< 0.1%
6138861
< 0.1%
6138841
< 0.1%
6138831
< 0.1%
6138821
< 0.1%
Distinct548342
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
Minimum2020-01-02 08:00:21.000001
Maximum2021-02-21 12:24:08.999997
2021-02-26T16:34:59.421661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:59.555374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Exit_Reason
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
AgentAnswered
562100 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters7307300
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAgentAnswered
2nd rowAgentAnswered
3rd rowAgentAnswered
4th rowAgentAnswered
5th rowAgentAnswered
ValueCountFrequency (%)
AgentAnswered562100
100.0%
2021-02-26T16:34:59.907714image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-26T16:34:59.969658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
agentanswered562100
100.0%

Most occurring characters

ValueCountFrequency (%)
e1686300
23.1%
A1124200
15.4%
n1124200
15.4%
g562100
 
7.7%
t562100
 
7.7%
s562100
 
7.7%
w562100
 
7.7%
r562100
 
7.7%
d562100
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6183100
84.6%
Uppercase Letter1124200
 
15.4%

Most frequent character per category

ValueCountFrequency (%)
e1686300
27.3%
n1124200
18.2%
g562100
 
9.1%
t562100
 
9.1%
s562100
 
9.1%
w562100
 
9.1%
r562100
 
9.1%
d562100
 
9.1%
ValueCountFrequency (%)
A1124200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7307300
100.0%

Most frequent character per script

ValueCountFrequency (%)
e1686300
23.1%
A1124200
15.4%
n1124200
15.4%
g562100
 
7.7%
t562100
 
7.7%
s562100
 
7.7%
w562100
 
7.7%
r562100
 
7.7%
d562100
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII7307300
100.0%

Most frequent character per block

ValueCountFrequency (%)
e1686300
23.1%
A1124200
15.4%
n1124200
15.4%
g562100
 
7.7%
t562100
 
7.7%
s562100
 
7.7%
w562100
 
7.7%
r562100
 
7.7%
d562100
 
7.7%

Party_Name
Categorical

HIGH CARDINALITY

Distinct147
Distinct (%)< 0.1%
Missing16
Missing (%)< 0.1%
Memory size4.3 MiB
Alex Dillon
 
16744
Daniel Schirmer
 
13918
Dave Lee Wincek
 
13590
John Gene Vura
 
13257
Alex Baltas
 
12996
Other values (142)
491579 

Length

Max length17
Median length16
Mean length15.37884017
Min length8

Characters and Unicode

Total characters8644200
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowKyle Younglas
2nd rowLuis Torres
3rd rowDaniel Schirmer
4th rowMike Bilfield
5th rowChris Smucny
ValueCountFrequency (%)
Alex Dillon 16744
 
3.0%
Daniel Schirmer 13918
 
2.5%
Dave Lee Wincek13590
 
2.4%
John Gene Vura13257
 
2.4%
Alex Baltas 12996
 
2.3%
Timmy Moran 12857
 
2.3%
Butch Herten 12424
 
2.2%
Mike Pascaru 12390
 
2.2%
Dustin Pollock 11732
 
2.1%
Dan Terbrack 11386
 
2.0%
Other values (137)430790
76.6%
2021-02-26T16:35:00.237376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
alex58681
 
5.1%
mike46134
 
4.0%
john26111
 
2.3%
matt22635
 
2.0%
dan22432
 
1.9%
dillon20301
 
1.8%
joe19642
 
1.7%
connor18112
 
1.6%
daniel16419
 
1.4%
schirmer16418
 
1.4%
Other values (146)884130
76.8%

Most occurring characters

ValueCountFrequency (%)
2222207
25.7%
e724093
 
8.4%
a580071
 
6.7%
n516805
 
6.0%
r445569
 
5.2%
l403147
 
4.7%
o371993
 
4.3%
i370230
 
4.3%
t258391
 
3.0%
c191036
 
2.2%
Other values (41)2560658
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5249661
60.7%
Space Separator2222207
25.7%
Uppercase Letter1171922
 
13.6%
Other Punctuation410
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e724093
13.8%
a580071
11.0%
n516805
9.8%
r445569
 
8.5%
l403147
 
7.7%
o371993
 
7.1%
i370230
 
7.1%
t258391
 
4.9%
c191036
 
3.6%
h190793
 
3.6%
Other values (15)1197533
22.8%
ValueCountFrequency (%)
M159310
13.6%
S105566
9.0%
D104942
9.0%
B101363
 
8.6%
A95994
 
8.2%
C93139
 
7.9%
J80590
 
6.9%
T65411
 
5.6%
K65282
 
5.6%
L40930
 
3.5%
Other values (14)259395
22.1%
ValueCountFrequency (%)
2222207
100.0%
ValueCountFrequency (%)
*410
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6421583
74.3%
Common2222617
 
25.7%

Most frequent character per script

ValueCountFrequency (%)
e724093
 
11.3%
a580071
 
9.0%
n516805
 
8.0%
r445569
 
6.9%
l403147
 
6.3%
o371993
 
5.8%
i370230
 
5.8%
t258391
 
4.0%
c191036
 
3.0%
h190793
 
3.0%
Other values (39)2369455
36.9%
ValueCountFrequency (%)
2222207
> 99.9%
*410
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8644200
100.0%

Most frequent character per block

ValueCountFrequency (%)
2222207
25.7%
e724093
 
8.4%
a580071
 
6.7%
n516805
 
6.0%
r445569
 
5.2%
l403147
 
4.7%
o371993
 
4.3%
i370230
 
4.3%
t258391
 
3.0%
c191036
 
2.2%
Other values (41)2560658
29.6%

channel
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
SEO
412579 
PPC
149521 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1686300
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSEO
2nd rowSEO
3rd rowSEO
4th rowSEO
5th rowSEO
ValueCountFrequency (%)
SEO412579
73.4%
PPC149521
 
26.6%
2021-02-26T16:35:00.428277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-26T16:35:00.491036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
seo412579
73.4%
ppc149521
 
26.6%

Most occurring characters

ValueCountFrequency (%)
S412579
24.5%
E412579
24.5%
O412579
24.5%
P299042
17.7%
C149521
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1686300
100.0%

Most frequent character per category

ValueCountFrequency (%)
S412579
24.5%
E412579
24.5%
O412579
24.5%
P299042
17.7%
C149521
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
Latin1686300
100.0%

Most frequent character per script

ValueCountFrequency (%)
S412579
24.5%
E412579
24.5%
O412579
24.5%
P299042
17.7%
C149521
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1686300
100.0%

Most frequent character per block

ValueCountFrequency (%)
S412579
24.5%
E412579
24.5%
O412579
24.5%
P299042
17.7%
C149521
 
8.9%

QueuedTime
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct928
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.71296389
Minimum0
Maximum5040
Zeros32969
Zeros (%)5.9%
Memory size4.3 MiB
2021-02-26T16:35:00.568836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median4
Q312
95-th percentile80
Maximum5040
Range5040
Interquartile range (IQR)9

Descriptive statistics

Standard deviation49.86228817
Coefficient of variation (CV)2.815016645
Kurtosis552.1475594
Mean17.71296389
Median Absolute Deviation (MAD)2
Skewness13.24491114
Sum9956457
Variance2486.247781
MonotocityNot monotonic
2021-02-26T16:35:00.700514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4123031
21.9%
374225
13.2%
255311
 
9.8%
536214
 
6.4%
134414
 
6.1%
032969
 
5.9%
612298
 
2.2%
711041
 
2.0%
910990
 
2.0%
810560
 
1.9%
Other values (918)161047
28.7%
ValueCountFrequency (%)
032969
 
5.9%
134414
 
6.1%
255311
9.8%
374225
13.2%
4123031
21.9%
ValueCountFrequency (%)
50401
< 0.1%
49381
< 0.1%
31901
< 0.1%
27501
< 0.1%
24171
< 0.1%

RingTime
Real number (ℝ≥0)

HIGH CORRELATION

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.032798434
Minimum0
Maximum29
Zeros1769
Zeros (%)0.3%
Memory size4.3 MiB
2021-02-26T16:35:00.819590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median7
Q310
95-th percentile16
Maximum29
Range29
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.260604147
Coefficient of variation (CV)0.5304009782
Kurtosis0.486628499
Mean8.032798434
Median Absolute Deviation (MAD)3
Skewness0.8183104623
Sum4515236
Variance18.1527477
MonotocityNot monotonic
2021-02-26T16:35:00.925813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
658942
10.5%
556971
10.1%
753282
9.5%
452040
9.3%
851126
9.1%
944905
 
8.0%
1037560
 
6.7%
336281
 
6.5%
1130556
 
5.4%
1224305
 
4.3%
Other values (20)116132
20.7%
ValueCountFrequency (%)
01769
 
0.3%
17842
 
1.4%
222382
4.0%
336281
6.5%
452040
9.3%
ValueCountFrequency (%)
292
 
< 0.1%
282
 
< 0.1%
273
 
< 0.1%
2697
< 0.1%
25146
< 0.1%

TalkTime
Real number (ℝ≥0)

ZEROS

Distinct1760
Distinct (%)0.3%
Missing1537
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean195.5202056
Minimum0
Maximum5791
Zeros7681
Zeros (%)1.4%
Memory size4.3 MiB
2021-02-26T16:35:01.057883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q151
median139
Q3259
95-th percentile610
Maximum5791
Range5791
Interquartile range (IQR)208

Descriptive statistics

Standard deviation202.2350432
Coefficient of variation (CV)1.034343446
Kurtosis8.570580301
Mean195.5202056
Median Absolute Deviation (MAD)97
Skewness2.207121686
Sum109601393
Variance40899.0127
MonotocityNot monotonic
2021-02-26T16:35:01.219000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07681
 
1.4%
204229
 
0.8%
194119
 
0.7%
184116
 
0.7%
214102
 
0.7%
223959
 
0.7%
173957
 
0.7%
233826
 
0.7%
243796
 
0.7%
163776
 
0.7%
Other values (1750)517002
92.0%
ValueCountFrequency (%)
07681
1.4%
11793
 
0.3%
22001
 
0.4%
31836
 
0.3%
41547
 
0.3%
ValueCountFrequency (%)
57911
< 0.1%
36071
< 0.1%
35471
< 0.1%
32231
< 0.1%
31771
< 0.1%

HoldTime
Real number (ℝ≥0)

MISSING
ZEROS

Distinct636
Distinct (%)0.1%
Missing24827
Missing (%)4.4%
Infinite0
Infinite (%)0.0%
Mean7.814933935
Minimum0
Maximum1789
Zeros431308
Zeros (%)76.7%
Memory size4.3 MiB
2021-02-26T16:35:01.355363image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile53
Maximum1789
Range1789
Interquartile range (IQR)0

Descriptive statistics

Standard deviation34.66194998
Coefficient of variation (CV)4.435347793
Kurtosis119.8265529
Mean7.814933935
Median Absolute Deviation (MAD)0
Skewness8.272977537
Sum4198753
Variance1201.450777
MonotocityNot monotonic
2021-02-26T16:35:01.481132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0431308
76.7%
121636
 
3.8%
210548
 
1.9%
38271
 
1.5%
45674
 
1.0%
54308
 
0.8%
63573
 
0.6%
72757
 
0.5%
82139
 
0.4%
91450
 
0.3%
Other values (626)45609
 
8.1%
(Missing)24827
 
4.4%
ValueCountFrequency (%)
0431308
76.7%
121636
 
3.8%
210548
 
1.9%
38271
 
1.5%
45674
 
1.0%
ValueCountFrequency (%)
17891
< 0.1%
15981
< 0.1%
12471
< 0.1%
12031
< 0.1%
10791
< 0.1%

WrapTime
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct2560
Distinct (%)0.5%
Missing14518
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean151.5060466
Minimum0
Maximum227003
Zeros10922
Zeros (%)1.9%
Memory size4.3 MiB
2021-02-26T16:35:01.617933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q117
median83
Q3195
95-th percentile525
Maximum227003
Range227003
Interquartile range (IQR)178

Descriptive statistics

Standard deviation383.5361301
Coefficient of variation (CV)2.531490582
Kurtosis223520.4677
Mean151.5060466
Median Absolute Deviation (MAD)74
Skewness379.2244211
Sum82961984
Variance147099.9631
MonotocityNot monotonic
2021-02-26T16:35:01.741290image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
415006
 
2.7%
513968
 
2.5%
313454
 
2.4%
611988
 
2.1%
010922
 
1.9%
710029
 
1.8%
88851
 
1.6%
97579
 
1.3%
106829
 
1.2%
116006
 
1.1%
Other values (2550)442950
78.8%
(Missing)14518
 
2.6%
ValueCountFrequency (%)
010922
1.9%
12021
 
0.4%
25679
 
1.0%
313454
2.4%
415006
2.7%
ValueCountFrequency (%)
2270031
< 0.1%
103861
< 0.1%
98341
< 0.1%
98171
< 0.1%
97261
< 0.1%

WaitTime
Real number (ℝ≥0)

HIGH CORRELATION

Distinct946
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.74576232
Minimum0
Maximum5051
Zeros1303
Zeros (%)0.2%
Memory size4.3 MiB
2021-02-26T16:35:02.008766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q110
median13
Q322
95-th percentile85
Maximum5051
Range5051
Interquartile range (IQR)12

Descriptive statistics

Standard deviation49.42966034
Coefficient of variation (CV)1.91991442
Kurtosis571.3819238
Mean25.74576232
Median Absolute Deviation (MAD)5
Skewness13.49560162
Sum14471693
Variance2443.291322
MonotocityNot monotonic
2021-02-26T16:35:02.153537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1043474
 
7.7%
940850
 
7.3%
1238263
 
6.8%
1137335
 
6.6%
836361
 
6.5%
1431375
 
5.6%
1329065
 
5.2%
726358
 
4.7%
1623190
 
4.1%
1520746
 
3.7%
Other values (936)235083
41.8%
ValueCountFrequency (%)
01303
 
0.2%
1117
 
< 0.1%
2276
 
< 0.1%
3532
 
0.1%
44193
0.7%
ValueCountFrequency (%)
50511
< 0.1%
49461
< 0.1%
31971
< 0.1%
27521
< 0.1%
24251
< 0.1%

Queue + Ring
Real number (ℝ≥0)

HIGH CORRELATION

Distinct946
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.74576232
Minimum0
Maximum5051
Zeros1303
Zeros (%)0.2%
Memory size4.3 MiB
2021-02-26T16:35:02.294869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q110
median13
Q322
95-th percentile85
Maximum5051
Range5051
Interquartile range (IQR)12

Descriptive statistics

Standard deviation49.42966034
Coefficient of variation (CV)1.91991442
Kurtosis571.3819238
Mean25.74576232
Median Absolute Deviation (MAD)5
Skewness13.49560162
Sum14471693
Variance2443.291322
MonotocityNot monotonic
2021-02-26T16:35:02.444400image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1043474
 
7.7%
940850
 
7.3%
1238263
 
6.8%
1137335
 
6.6%
836361
 
6.5%
1431375
 
5.6%
1329065
 
5.2%
726358
 
4.7%
1623190
 
4.1%
1520746
 
3.7%
Other values (936)235083
41.8%
ValueCountFrequency (%)
01303
 
0.2%
1117
 
< 0.1%
2276
 
< 0.1%
3532
 
0.1%
44193
0.7%
ValueCountFrequency (%)
50511
< 0.1%
49461
< 0.1%
31971
< 0.1%
27521
< 0.1%
24251
< 0.1%

WaitTime vs QueuedTime
Real number (ℝ≥0)

HIGH CORRELATION

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.032798434
Minimum0
Maximum29
Zeros1769
Zeros (%)0.3%
Memory size4.3 MiB
2021-02-26T16:35:02.571265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median7
Q310
95-th percentile16
Maximum29
Range29
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.260604147
Coefficient of variation (CV)0.5304009782
Kurtosis0.486628499
Mean8.032798434
Median Absolute Deviation (MAD)3
Skewness0.8183104623
Sum4515236
Variance18.1527477
MonotocityNot monotonic
2021-02-26T16:35:02.678800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
658942
10.5%
556971
10.1%
753282
9.5%
452040
9.3%
851126
9.1%
944905
 
8.0%
1037560
 
6.7%
336281
 
6.5%
1130556
 
5.4%
1224305
 
4.3%
Other values (20)116132
20.7%
ValueCountFrequency (%)
01769
 
0.3%
17842
 
1.4%
222382
4.0%
336281
6.5%
452040
9.3%
ValueCountFrequency (%)
292
 
< 0.1%
282
 
< 0.1%
273
 
< 0.1%
2697
< 0.1%
25146
< 0.1%

Interactions

2021-02-26T16:34:40.801571image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:41.130389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:41.340001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:41.553338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:41.780437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:42.036145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:42.243025image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:42.459863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:42.664381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:42.871978image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:43.092937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:43.290897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:43.491450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:43.690380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:43.893335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:44.118557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:44.312387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:44.507452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:44.731393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:44.930594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:45.150096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:45.353666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:45.558017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:45.759705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:45.971206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:46.188708image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:46.410097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:46.615585image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:46.834344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:47.047243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:47.410481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:47.615454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:47.810470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:48.035894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:48.249743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:48.464878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:48.683227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:48.890189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:49.083064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:49.297762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:49.553201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:49.753651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:49.962434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:50.189524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:50.382356image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:50.589092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:50.772689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:50.968421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:51.188938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:51.415941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:51.614941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:51.814125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:52.004727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:52.205875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:52.391916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:52.591126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:52.782807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:52.979554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:53.191836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:53.535352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:53.759662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:53.962527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:54.156320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:54.354818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:54.549469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:54.750998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:54.958257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:55.165246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:55.361145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:55.562893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:55.795494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T16:34:56.003425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-26T16:35:02.803253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-26T16:35:02.970263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-26T16:35:03.139675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-26T16:35:03.306804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-26T16:35:03.460259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-26T16:34:56.550052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-26T16:34:57.212952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-26T16:34:58.299075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-26T16:34:58.520608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexCall_StartExit_ReasonParty_NamechannelQueuedTimeRingTimeTalkTimeHoldTimeWrapTimeWaitTimeQueue + RingWaitTime vs QueuedTime
0132020-01-02 08:00:21.000001AgentAnsweredKyle YounglasSEO09184.00.0318.0999
1142020-01-02 08:00:56.000001AgentAnsweredLuis TorresSEO05454.00.015.0555
2162020-01-02 08:02:52.000002AgentAnsweredDaniel SchirmerSEO111411.00.095.0121211
3182020-01-02 08:03:16.000004AgentAnsweredMike BilfieldSEO06182.089.08.0666
4192020-01-02 08:03:31.000003AgentAnsweredChris SmucnySEO15428.00.035.0665
5202020-01-02 08:03:46.000002AgentAnsweredIan FlanaganSEO0827.03.015.0888
6212020-01-02 08:04:21.000003AgentAnsweredKenneth CombesPPC19522.044.092.010109
7222020-01-02 08:05:00.000004AgentAnsweredMac BlankSEO1356.02.010.0443
8232020-01-02 08:05:38.000003AgentAnsweredAlex ShroyerSEO11514.02.022.0161615
9242020-01-02 08:07:53.000003AgentAnsweredMichael DiZonnoPPC08100.00.05.0888

Last rows

df_indexCall_StartExit_ReasonParty_NamechannelQueuedTimeRingTimeTalkTimeHoldTimeWrapTimeWaitTimeQueue + RingWaitTime vs QueuedTime
5620906138772021-02-21 12:05:26.999998AgentAnsweredJohnathan JordanSEO1212132.00.00.0242412
5620916138782021-02-21 12:07:37.000004AgentAnsweredMatthew DuffSEO1111210.00.00.0222211
5620926138792021-02-21 12:08:27.000004AgentAnsweredJunior FetchetSEO88133.00.02.016168
5620936138802021-02-21 12:13:19.000001AgentAnsweredMatt TashjianSEO151568.00.042.0303015
5620946138812021-02-21 12:13:54.000002AgentAnsweredMatthew DuffSEO18692.00.02.024246
5620956138822021-02-21 12:14:41.999997AgentAnsweredLauren BaschSEO17540.00.00.022225
5620966138832021-02-21 12:17:38.999996AgentAnsweredMatt TashjianSEO1414121.00.02.0282814
5620976138842021-02-21 12:18:55.000003AgentAnsweredMeredith ChesneySEO44289.00.01.0884
5620986138862021-02-21 12:22:32.999998AgentAnsweredMatt TashjianSEO171755.00.012.0343417
5620996138872021-02-21 12:24:08.999997AgentAnsweredJunior FetchetSEO77201.00.042.014147